Johan Philips

A Brain-Actuated Wheelchair: Asynchronous and Non-Invasive Brain-Computer Interfaces for Continuous Control of Robots

OBJECTIVE To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. METHODS In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1h to 2months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. RESULTS In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. CONCLUSIONS The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. SIGNIFICANCE These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.

Adaptive Shared Control of a Brain-Actuated Simulated Wheelchair

The use of shared control techniques has a profound impact on the performance of a robotic assistant controlled by human brain signals. However, this shared control usually provides assistance to the user in a constant and identical manner each time. Creating an adaptive level of assistance, thereby complementing the users capabilities at any moment, would be more appropriate. The better the user can do by himself, the less assistance he receives from the shared control system; and vice versa. In order to do this, we need to be able to detect when and in what way the user needs assistance. An appropriate assisting behaviour would then be activated for the time the user requires help, thereby adapting the level of assistance to the specific situation. This paper presents such a system, helping a brain-computer interface (BCI) subject perform goal-directed navigation of a simulated wheelchair in an adaptive manner. Whenever the subject has more difficulties in driving the wheelchair, more assistance will be given. Experimental results of two subjects show that this adaptive shared control increases the task performance. Also, it shows that a subject with a lower BCI performance has more need for extra assistance in difficult situations, such as manoeuvring in a narrow corridor.

Asynchronous non-invasive brain-actuated control of an intelligent wheelchair

In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external cue. To do so the users learn to voluntary modulate EEG oscillatory rhythms by executing three mental tasks (i.e., mental imagery) that are associated to different steering commands. Importantly, we implement shared control techniques between the BMI and the intelligent wheelchair to assist the subject in the driving task. The results show that the three subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair.

Context-based filtering for assisted brain-actuated wheelchair driving

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subjects steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

Bayesian plan recognition and shared control under uncertainty: assisting wheelchair drivers by tracking fine motion paths

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the users true intent out of potentially noisy steering signals before assisting him/her. The users driving performance is explicitly modeled in order to recognize the users intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuvens intelligent wheelchair Sharioto.

A Service-Oriented Approach for Holonic Manufacturing Control and Beyond

The Holonic Manufacturing Execution System (HMES), developed at K.U.Leuven, utilizes a service-oriented approach to control manufacturing operations in real time. This chapter first explains how manufacturing control emerges from interaction between intelligent products and intelligent resources. Services play a key role in this interaction and form a decoupling point between the generic control system and application-specific elements. To illustrate that this service-oriented approach allows applying the same concepts and principles to various domains, several applications in manufacturing, open-air engineering, robotics and logistics are described. Finally, the chapter describes how supporting services, such as maintenance, can be seamlessly integrated with the core activities of the system.

Cooperation between a Holonic Logistics Execution System and a Vehicle Routing Scheduling System

The organization of the logistic operations in a cross-dock is complex. Cross-dock managers can be supported in this task by a logistics execution system (LES). A holonic LES uses a self-organizing and decentralized approach to control the cross-docking operations. Based on multi-agent technology, this approach aims to improve the robustness against disturbances. A drawback of this approach is however that this decentralized LES has no complete view on the system, in contrast to a centralized scheduling system. This paper presents the cooperation between a holonic LES and a vehicle routing scheduling system in order to combine the benefits of both systems. The holonic LES can benefit from the provided schedule to improve the global performance, while allowing the schedule to be executed, compensating for small deviations and possible simplifications. The results of some preliminary simulation experiments indicate that this cooperation can be realized and improves the global performance.

PROSA and delegate MAS in robotics

This paper discusses the application of the PROSA reference architecture [1] and its delegate multi-agent system coordination mechanism or D-MAS [2], [3] to robotic applications, particularly to the coordination of multiple mobile robots. Originally, PROSA and D-MAS have been developed for the manufacturing executions system or MES domain [4]. The application of PROSA to robotics is a generalization: PROSA has been elaborated but was never in need of modification. This reference architecture is applied to multiple application domains and differences are confined to its elaboration, specialization and instantiation. Likewise, the delegate MAS mechanism was reused across application domains. As a side effect, this facilitates the seamless integration of manufacturing execution systems and the coordination of mobile robots if they are PROSA and D-MAS implementations. This paper first presents the mapping of PROSA onto robotics. Next, it discusses how this technology from the MES domain enhances performance in mobile robot applications.

Backwards maneuvering powered wheelchairs with haptic guidance

This paper describes a novel haptic guidance scheme that helps powered wheelchair users steer their wheelchair through narrow and complex environments. The proposed scheme encodes the local environment of the wheelchair as a set of collision-free circular paths. An adaptive impedance controller is constructed upon these circular paths. The controller increases resistance when nearing obstacles and simultaneously helps the user to change motion towards a safer circular path. To test the algorithm, a commercial powered wheelchair was interfaced and equipped with necessary sensors and an in-house built haptic joystick. The user was asked to drive backwards into a narrow elevator with and without navigation assistance. Although there is still room for improvements, the first results are promising. Thanks to the assistance the user can perform this maneuver successfully in most of the cases without even looking backwards.

Computational Complexity and Scalability Analysis of PROSA and delegate MAS

This paper discusses the results of the computational complexity analysis with respect to time, which was performed on the Holonic Manufacturing Execution System (HMES) following the Product-Resource-Order-Staff Architecture (PROSA) and delegate Multi-Agent System (D-MAS). A practical approach was used instead of a theoretical or formal analysis. The analysis shows a polynomial relationship between the time complexity and number of resources and orders in the system and highlights where optimisations could improve the current implementation. Scalability experiments in the domain of multi-robot navigation, with respect to number of robots and environment size, shows this complexity is an upper bound.